{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {i: np.random.randn() for i in range(8)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.37341286986000277,\n",
       " 1: 0.41759434366637505,\n",
       " 2: -1.0561331590958778,\n",
       " 3: -0.8731623066174738,\n",
       " 4: 0.19723401038027635,\n",
       " 5: -2.768164201349259,\n",
       " 6: -1.3410039541548828,\n",
       " 7: -0.4279571819097882}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fd08842308>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 2 * np.pi, num=100)\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
